On account of the exponential augmentation of documents on the internet, users need all the pertinent data at ?1? place with no hassle. Therefore, automatic text summarization (ATS) is needed to automate the procedure of summarizing text via extorting the salient details as of the documents. The goal is to propose an automatic, generic, in addition to extractive text summarization for a single document utilizing Deep Learning Modifier Neural Network (DLMNN) classifier for generating an adequately informative summary centered upon the entropy values. A proposed DLMNN framework comprises ?6? phases. In the initial phase, the input document is pre-processed which engages stop word removal, tokenization, along with stemming. Subsequently, the features are extorted as of the pre-processed data. Next, the most apposite features are selected employing the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed utilizing support as well as confident measure. Afterward, DLMNN classifier is utilized to classify these values into ?2? classes, a) highest entropy values and b) lowest entropy values. Lastly, the class that holds the highest entropy values are chosen besides, the informative sentences are selected as of the highest entropy values to form the last summary. Experimental outcomes are executed and the proposed DLMNN classifier?s performance is analyzed utilizing sensitivity, accuracy, recall, specificity, precision, and also f-measure. The proposed DLMNN provides the best outcomes amid all other techniques.

Abstract. The present article puts forward a method for an interactive model generation through the use of Genetic Algorithms applied to small populations. Micropopulations actually worsen the problem of the premature convergence of the algorithm, since genetic diversity is very limited. In addition, some key factors, which modify the changing likelihood of alleles, cause the likelihood of premature convergence to decrease. The present technique has been applied to the design of 3D models, starting from generic and standard pieces, using objective searches and searches with no defined objective.

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The development of applications for mobile devices is a constantly growing market which and more and more enterprises support the development of applications for this kind of devices. In that sense, videogames for mobile devices have become very popular worldwide and are now part of highly profitable and competitive industry. Due to the diversity of platforms and mobile devices and the complexity of this kind of applications, the development time and the number of errors within that development process have increased. The productivity of the developers has also decreased due to the necessity of using many programming languages in the development process. One of the most popular strategies is to employ specialized people to perform the development tasks more efficiently, but this involves an increase of the costs, which makes some applications economically unviable. In this article we present the Gade4all Project, consisting in a new platform that aims to facilitate the development of videogames and entertainment software through the use of Domain Specific Languages and Model Driven Engineering. This tool makes possible for users without previous knowledge in the field of software development to create 2D videogames for multiplatform mobile devices in a simple and innovative way